Kernel Methods for Nonlinear Discriminative Data Analysis
Identifieur interne : 001789 ( Istex/Corpus ); précédent : 001788; suivant : 001790Kernel Methods for Nonlinear Discriminative Data Analysis
Auteurs : Xiuwen Liu ; Washington MioSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.
Url:
DOI: 10.1007/11585978_38
Links to Exploration step
ISTEX:0DBA3A1211F38D7A24362213C22F4BFB7EA014E7Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Kernel Methods for Nonlinear Discriminative Data Analysis</title><author><name sortKey="Liu, Xiuwen" sort="Liu, Xiuwen" uniqKey="Liu X" first="Xiuwen" last="Liu">Xiuwen Liu</name><affiliation><mods:affiliation>Department of Computer Science, Florida State University, 32306, Tallahassee</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: liux@cs.fsu.edu</mods:affiliation></affiliation></author><author><name sortKey="Mio, Washington" sort="Mio, Washington" uniqKey="Mio W" first="Washington" last="Mio">Washington Mio</name><affiliation><mods:affiliation>Department of Mathematics, Florida State University, 32306, Tallahassee, FL, USA</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:0DBA3A1211F38D7A24362213C22F4BFB7EA014E7</idno><date when="2005" year="2005">2005</date><idno type="doi">10.1007/11585978_38</idno><idno type="url">https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001789</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Kernel Methods for Nonlinear Discriminative Data Analysis</title><author><name sortKey="Liu, Xiuwen" sort="Liu, Xiuwen" uniqKey="Liu X" first="Xiuwen" last="Liu">Xiuwen Liu</name><affiliation><mods:affiliation>Department of Computer Science, Florida State University, 32306, Tallahassee</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: liux@cs.fsu.edu</mods:affiliation></affiliation></author><author><name sortKey="Mio, Washington" sort="Mio, Washington" uniqKey="Mio W" first="Washington" last="Mio">Washington Mio</name><affiliation><mods:affiliation>Department of Mathematics, Florida State University, 32306, Tallahassee, FL, USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2005</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">0DBA3A1211F38D7A24362213C22F4BFB7EA014E7</idno><idno type="DOI">10.1007/11585978_38</idno><idno type="ChapterID">38</idno><idno type="ChapterID">Chap38</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Xiuwen Liu</name><affiliations><json:string>Department of Computer Science, Florida State University, 32306, Tallahassee</json:string><json:string>E-mail: liux@cs.fsu.edu</json:string></affiliations></json:item><json:item><name>Washington Mio</name><affiliations><json:string>Department of Mathematics, Florida State University, 32306, Tallahassee, FL, USA</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.</abstract><qualityIndicators><score>6.248</score><pdfVersion>1.3</pdfVersion><pdfPageSize>430 x 660 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>722</abstractCharCount><pdfWordCount>5383</pdfWordCount><pdfCharCount>29423</pdfCharCount><pdfPageCount>16</pdfPageCount><abstractWordCount>104</abstractWordCount></qualityIndicators><title>Kernel Methods for Nonlinear Discriminative Data Analysis</title><genre.original><json:string>OriginalPaper</json:string></genre.original><chapterId><json:string>38</json:string><json:string>Chap38</json:string></chapterId><genre><json:string>conference [eBooks]</json:string></genre><serie><volume>IV</volume><editor><json:item><name>David Hutchison</name><affiliations><json:string>Lancaster University, UK</json:string></affiliations></json:item><json:item><name>Takeo Kanade</name><affiliations><json:string>Carnegie Mellon University, Pittsburgh, PA, USA</json:string></affiliations></json:item><json:item><name>Josef Kittler</name><affiliations><json:string>University of Surrey, Guildford, UK</json:string></affiliations></json:item><json:item><name>Jon M. Kleinberg</name><affiliations><json:string>Cornell University, Ithaca, NY, USA</json:string></affiliations></json:item><json:item><name>Friedemann Mattern</name><affiliations><json:string>ETH Zurich, Switzerland</json:string></affiliations></json:item><json:item><name>John C. Mitchell</name><affiliations><json:string>Stanford University, CA, USA</json:string></affiliations></json:item><json:item><name>Moni Naor</name><affiliations><json:string>Weizmann Institute of Science, Rehovot, Israel</json:string></affiliations></json:item><json:item><name>Oscar Nierstrasz</name><affiliations><json:string>University of Bern, Switzerland</json:string></affiliations></json:item><json:item><name>C. Pandu Rangan</name><affiliations><json:string>Indian Institute of Technology, Madras, India</json:string></affiliations></json:item><json:item><name>Bernhard Steffen</name><affiliations><json:string>University of Dortmund, Germany</json:string></affiliations></json:item><json:item><name>Madhu Sudan</name><affiliations><json:string>Massachusetts Institute of Technology, MA, USA</json:string></affiliations></json:item><json:item><name>Demetri Terzopoulos</name><affiliations><json:string>New York University, NY, USA</json:string></affiliations></json:item><json:item><name>Dough Tygar</name><affiliations><json:string>University of California, Berkeley, CA, USA</json:string></affiliations></json:item><json:item><name>Moshe Y. Vardi</name><affiliations><json:string>Rice University, Houston, TX, USA</json:string></affiliations></json:item><json:item><name>Gerhard Weikum</name><affiliations><json:string>Max-Planck Institute of Computer Science, Saarbruecken, Germany</json:string></affiliations></json:item></editor><issn><json:string>0302-9743</json:string></issn><language><json:string>unknown</json:string></language><eissn><json:string>1611-3349</json:string></eissn><title>Lecture Notes in Computer Science</title><copyrightDate>2005</copyrightDate></serie><host><editor><json:item><name>Anand Rangarajan</name><affiliations><json:string>Department of CISE, University of Florida, Gainesville, FL, USA</json:string><json:string>E-mail: anand@cise.ufl.edu</json:string></affiliations></json:item><json:item><name>Baba Vemuri</name><affiliations><json:string>University of Florida, 32611, Gainesville, FL, USA</json:string><json:string>E-mail: vemuri@cise.ufl.edu</json:string></affiliations></json:item><json:item><name>Alan L. Yuille</name><affiliations><json:string>Department of Statistics, University of California, Los Angeles</json:string><json:string>E-mail: yuille@stat.ucla.edu</json:string></affiliations></json:item></editor><subject><json:item><value>Computer Science</value></json:item><json:item><value>Computer Science</value></json:item><json:item><value>Pattern Recognition</value></json:item><json:item><value>Image Processing and Computer Vision</value></json:item><json:item><value>Artificial Intelligence (incl. Robotics)</value></json:item><json:item><value>Computer Graphics</value></json:item><json:item><value>Algorithm Analysis and Problem Complexity</value></json:item><json:item><value>Computation by Abstract Devices</value></json:item></subject><isbn><json:string>978-3-540-30287-2</json:string></isbn><language><json:string>unknown</json:string></language><eissn><json:string>1611-3349</json:string></eissn><title>Energy Minimization Methods in Computer Vision and Pattern Recognition</title><genre.original><json:string>Proceedings</json:string></genre.original><bookId><json:string>978-3-540-32098-2</json:string></bookId><volume>3757</volume><pages><last>599</last><first>584</first></pages><issn><json:string>0302-9743</json:string></issn><genre><json:string>Book Series</json:string></genre><eisbn><json:string>978-3-540-32098-2</json:string></eisbn><copyrightDate>2005</copyrightDate><doi><json:string>10.1007/11585978</json:string></doi></host><publicationDate>2005</publicationDate><copyrightDate>2005</copyrightDate><doi><json:string>10.1007/11585978_38</json:string></doi><id>0DBA3A1211F38D7A24362213C22F4BFB7EA014E7</id><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/fulltext/pdf</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Kernel Methods for Nonlinear Discriminative Data Analysis</title><respStmt xml:id="ISTEX-API" resp="Références bibliographiques récupérées via GROBID" name="ISTEX-API (INIST-CNRS)"></respStmt></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin, Heidelberg</pubPlace><availability><p>SPRINGER</p></availability><date>2005</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Kernel Methods for Nonlinear Discriminative Data Analysis</title><author><persName><forename type="first">Xiuwen</forename><surname>Liu</surname></persName><email>liux@cs.fsu.edu</email><affiliation>Department of Computer Science, Florida State University, 32306, Tallahassee</affiliation></author><author><persName><forename type="first">Washington</forename><surname>Mio</surname></persName><affiliation>Department of Mathematics, Florida State University, 32306, Tallahassee, FL, USA</affiliation></author></analytic><monogr><title level="m">Energy Minimization Methods in Computer Vision and Pattern Recognition</title><title level="m" type="sub">5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005. Proceedings</title><idno type="pISBN">978-3-540-30287-2</idno><idno type="eISBN">978-3-540-32098-2</idno><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="DOI">10.1007/11585978</idno><idno type="BookID">978-3-540-32098-2</idno><idno type="BookTitleID">127700</idno><idno type="BookSequenceNumber">3757</idno><idno type="BookVolumeNumber">3757</idno><idno type="BookChapterCount">42</idno><editor><persName><forename type="first">Anand</forename><surname>Rangarajan</surname></persName><email>anand@cise.ufl.edu</email><affiliation>Department of CISE, University of Florida, Gainesville, FL, USA</affiliation></editor><editor><persName><forename type="first">Baba</forename><surname>Vemuri</surname></persName><email>vemuri@cise.ufl.edu</email><affiliation>University of Florida, 32611, Gainesville, FL, USA</affiliation></editor><editor><persName><forename type="first">Alan</forename><forename type="first">L.</forename><surname>Yuille</surname></persName><email>yuille@stat.ucla.edu</email><affiliation>Department of Statistics, University of California, Los Angeles</affiliation></editor><imprint><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin, Heidelberg</pubPlace><date type="published" when="2005"></date><biblScope unit="volume">3757</biblScope><biblScope unit="page" from="584">584</biblScope><biblScope unit="page" to="599">599</biblScope></imprint></monogr><series><title level="s">Lecture Notes in Computer Science</title><editor><persName><forename type="first">David</forename><surname>Hutchison</surname></persName><affiliation>Lancaster University, UK</affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation>University of Surrey, Guildford, UK</affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation>Cornell University, Ithaca, NY, USA</affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation>ETH Zurich, Switzerland</affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation>Stanford University, CA, USA</affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation>University of Bern, Switzerland</affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation>Indian Institute of Technology, Madras, India</affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Germany</affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>New York University, NY, USA</affiliation></editor><editor><persName><forename type="first">Dough</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation></editor><biblScope><date>2005</date></biblScope><biblScope unit="volume">IV</biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesId">558</idno></series><idno type="istex">0DBA3A1211F38D7A24362213C22F4BFB7EA014E7</idno><idno type="DOI">10.1007/11585978_38</idno><idno type="ChapterID">38</idno><idno type="ChapterID">Chap38</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2005</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I2203X</label><label>I22021</label><label>I21017</label><label>I22013</label><label>I16021</label><label>I16013</label><item><term>Computer Science</term></item><item><term>Pattern Recognition</term></item><item><term>Image Processing and Computer Vision</term></item><item><term>Artificial Intelligence (incl. Robotics)</term></item><item><term>Computer Graphics</term></item><item><term>Algorithm Analysis and Problem Complexity</term></item><item><term>Computation by Abstract Devices</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2005">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-3-20">References added</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin, Heidelberg</PublisherLocation></PublisherInfo><Series><SeriesInfo SeriesType="Series" TocLevels="0"><SeriesID>558</SeriesID><SeriesPrintISSN>0302-9743</SeriesPrintISSN><SeriesElectronicISSN>1611-3349</SeriesElectronicISSN><SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle></SeriesInfo><SeriesHeader><EditorGroup><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Hutchison</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff2"><EditorName DisplayOrder="Western"><GivenName>Takeo</GivenName><FamilyName>Kanade</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Josef</GivenName><FamilyName>Kittler</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff4"><EditorName DisplayOrder="Western"><GivenName>Jon</GivenName><GivenName>M.</GivenName><FamilyName>Kleinberg</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff5"><EditorName DisplayOrder="Western"><GivenName>Friedemann</GivenName><FamilyName>Mattern</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff6"><EditorName DisplayOrder="Western"><GivenName>John</GivenName><GivenName>C.</GivenName><FamilyName>Mitchell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff7"><EditorName DisplayOrder="Western"><GivenName>Moni</GivenName><FamilyName>Naor</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff8"><EditorName DisplayOrder="Western"><GivenName>Oscar</GivenName><FamilyName>Nierstrasz</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff9"><EditorName DisplayOrder="Western"><GivenName>C.</GivenName><FamilyName>Pandu Rangan</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff10"><EditorName DisplayOrder="Western"><GivenName>Bernhard</GivenName><FamilyName>Steffen</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff11"><EditorName DisplayOrder="Western"><GivenName>Madhu</GivenName><FamilyName>Sudan</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff12"><EditorName DisplayOrder="Western"><GivenName>Demetri</GivenName><FamilyName>Terzopoulos</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff13"><EditorName DisplayOrder="Western"><GivenName>Dough</GivenName><FamilyName>Tygar</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff14"><EditorName DisplayOrder="Western"><GivenName>Moshe</GivenName><GivenName>Y.</GivenName><FamilyName>Vardi</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff15"><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Weikum</FamilyName></EditorName></Editor><Affiliation ID="Aff1"><OrgName>Lancaster University</OrgName><OrgAddress><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>CA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32098-2</BookID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle><BookSubTitle>5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005. Proceedings</BookSubTitle><BookVolumeNumber>3757</BookVolumeNumber><BookSequenceNumber>3757</BookSequenceNumber><BookDOI>10.1007/11585978</BookDOI><BookTitleID>127700</BookTitleID><BookPrintISBN>978-3-540-30287-2</BookPrintISBN><BookElectronicISBN>978-3-540-32098-2</BookElectronicISBN><BookChapterCount>42</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I16013" Priority="6" Type="Secondary">Computation by Abstract Devices</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Anand</GivenName><FamilyName>Rangarajan</FamilyName></EditorName><Contact><Email>anand@cise.ufl.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Baba</GivenName><FamilyName>Vemuri</FamilyName></EditorName><Contact><Email>vemuri@cise.ufl.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Alan</GivenName><GivenName>L.</GivenName><FamilyName>Yuille</FamilyName></EditorName><Contact><Email>yuille@stat.ucla.edu</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of CISE</OrgDivision><OrgName>University of Florida</OrgName><OrgAddress><City>Gainesville</City><State>FL</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Florida</OrgName><OrgAddress><Postcode>32611</Postcode><City>Gainesville</City><State>FL</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Department of Statistics</OrgDivision><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartNumber>IV</PartNumber><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Other Approaches and Applications</PartTitle><PartChapterCount>8</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></PartContext></PartInfo><Chapter ID="Chap38" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>38</ChapterID><ChapterDOI>10.1007/11585978_38</ChapterDOI><ChapterSequenceNumber>38</ChapterSequenceNumber><ChapterTitle Language="En">Kernel Methods for Nonlinear Discriminative Data Analysis</ChapterTitle><ChapterFirstPage>584</ChapterFirstPage><ChapterLastPage>599</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>4</PartID><BookID>978-3-540-32098-2</BookID><BookTitle>Energy Minimization Methods in Computer Vision and Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Xiuwen</GivenName><FamilyName>Liu</FamilyName></AuthorName><Contact><Email>liux@cs.fsu.edu</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Washington</GivenName><FamilyName>Mio</FamilyName></AuthorName></Author><Affiliation ID="Aff19"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>Florida State University</OrgName><OrgAddress><City>Tallahassee</City><Postcode>32306</Postcode></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Department of Mathematics</OrgDivision><OrgName>Florida State University</OrgName><OrgAddress><City>Tallahassee</City><State>FL</State><Postcode>32306</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Kernel Methods for Nonlinear Discriminative Data Analysis</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Kernel Methods for Nonlinear Discriminative Data Analysis</title></titleInfo><name type="personal"><namePart type="given">Xiuwen</namePart><namePart type="family">Liu</namePart><affiliation>Department of Computer Science, Florida State University, 32306, Tallahassee</affiliation><affiliation>E-mail: liux@cs.fsu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Washington</namePart><namePart type="family">Mio</namePart><affiliation>Department of Mathematics, Florida State University, 32306, Tallahassee, FL, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.</abstract><relatedItem type="host"><titleInfo><title>Energy Minimization Methods in Computer Vision and Pattern Recognition</title><subTitle>5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Anand</namePart><namePart type="family">Rangarajan</namePart><affiliation>Department of CISE, University of Florida, Gainesville, FL, USA</affiliation><affiliation>E-mail: anand@cise.ufl.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Baba</namePart><namePart type="family">Vemuri</namePart><affiliation>University of Florida, 32611, Gainesville, FL, USA</affiliation><affiliation>E-mail: vemuri@cise.ufl.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Alan</namePart><namePart type="given">L.</namePart><namePart type="family">Yuille</namePart><affiliation>Department of Statistics, University of California, Los Angeles</affiliation><affiliation>E-mail: yuille@stat.ucla.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic></subject><identifier type="DOI">10.1007/11585978</identifier><identifier type="ISBN">978-3-540-30287-2</identifier><identifier type="eISBN">978-3-540-32098-2</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">127700</identifier><identifier type="BookID">978-3-540-32098-2</identifier><identifier type="BookChapterCount">42</identifier><identifier type="BookVolumeNumber">3757</identifier><identifier type="BookSequenceNumber">3757</identifier><identifier type="PartChapterCount">8</identifier><part><date>2005</date><detail type="part"><title>IV: Other Approaches and Applications</title></detail><detail type="volume"><number>3757</number><caption>vol.</caption></detail><extent unit="pages"><start>584</start><end>599</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>New York University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><part><detail type="volume"><number>IV</number><caption>vol.</caption></detail></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><identifier type="istex">0DBA3A1211F38D7A24362213C22F4BFB7EA014E7</identifier><identifier type="DOI">10.1007/11585978_38</identifier><identifier type="ChapterID">38</identifier><identifier type="ChapterID">Chap38</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2005</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></mods></metadata><enrichments><istex:refBibTEI uri="https://api.istex.fr/document/0DBA3A1211F38D7A24362213C22F4BFB7EA014E7/enrichments/refBib"><teiHeader></teiHeader><text><front></front><body></body><back><listBibl><biblStruct xml:id="b0"><analytic><title level="a" type="main">Kernel independent component analysis</title><author><persName><forename type="first">F</forename><surname>Bach</surname></persName></author><author><persName><forename type="first">M</forename><forename type="middle">I</forename><surname>Jordan</surname></persName></author></analytic><monogr><title level="j">Journal of Machine Learning Research</title><imprint><biblScope unit="volume">3</biblScope><biblScope unit="page" from="1" to="48"></biblScope><date type="published" when="2003"></date></imprint></monogr></biblStruct><biblStruct xml:id="b1"><analytic><title level="a" type="main">Generalized discriminant analysis using a kernel approach</title><author><persName><forename type="first">G</forename><surname>Baudat</surname></persName></author><author><persName><forename type="first">F</forename><surname>Anouar</surname></persName></author></analytic><monogr><title level="j">Neural Computation</title><imprint><biblScope unit="volume">12</biblScope><biblScope unit="page" from="2385" to="2404"></biblScope><date type="published" when="2000"></date></imprint></monogr></biblStruct><biblStruct xml:id="b2"><analytic><title level="a" type="main">Eigenfaces vs. fisherfaces: Recognition using class specific linear projection</title><author><persName><forename type="first">P</forename><forename type="middle">N</forename><surname>Belhumeur</surname></persName></author><author><persName><forename type="first">J</forename><forename type="middle">P</forename><surname>Hepanha</surname></persName></author><author><persName><forename type="first">D</forename><forename type="middle">J</forename><surname>Kriegman</surname></persName></author></analytic><monogr><title level="j">IEEE Transactions on Pattern Analysis and Machine Intelligence</title><imprint><biblScope unit="volume">19</biblScope><biblScope unit="issue">7</biblScope><biblScope unit="page" from="711" to="720"></biblScope><date type="published" when="1997"></date></imprint></monogr></biblStruct><biblStruct xml:id="b3"><analytic><title level="a" type="main">A training algorithm for optimal margin classifiers</title><author><persName><forename type="first">B</forename><surname>Boser</surname></persName></author><author><persName><forename type="first">I</forename><surname>Guyon</surname></persName></author><author><persName><forename type="first">V</forename><surname>Vapnik</surname></persName></author></analytic><monogr><title level="m">Proc. of the 5th Annual Workshop on Computational Learning Theory</title><meeting>. of the 5th Annual Workshop on Computational Learning Theory</meeting><imprint><date type="published" when="1992"></date><biblScope unit="page" from="144" to="152"></biblScope></imprint></monogr></biblStruct><biblStruct xml:id="b4"><monogr><title level="m" type="main">An Introduction to Differential Manifolds and Riemannian Geometry</title><author><persName><forename type="first">W</forename><forename type="middle">M</forename><surname>Boothby</surname></persName></author><imprint><date type="published" when="1986"></date><publisher>Academic Press</publisher></imprint></monogr></biblStruct><biblStruct xml:id="b5"><analytic><title level="a" type="main">Fast and robust fixed-point algorithm for independent component analysis</title><author><persName><forename type="first">A</forename><surname>Hyvarinen</surname></persName></author></analytic><monogr><title level="j">IEEE Transactions on Neural Networks</title><imprint><biblScope unit="volume">10</biblScope><biblScope unit="page" from="626" to="634"></biblScope><date type="published" when="1999"></date></imprint></monogr></biblStruct><biblStruct xml:id="b6"><analytic><title level="a" type="main">Optimal linear representations of images for object recognition</title><author><persName><forename type="first">X</forename><surname>Liu</surname></persName></author><author><persName><forename type="first">A</forename><surname>Srivastava</surname></persName></author><author><persName><forename type="first">Kyle</forename><surname>Gallivan</surname></persName></author></analytic><monogr><title level="j">IEEE Transactions on Pattern Recognition and Machine Intelligence</title><imprint><biblScope unit="volume">26</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="662" to="666"></biblScope><date type="published" when="2004"></date></imprint></monogr></biblStruct><biblStruct xml:id="b7"><analytic><title level="a" type="main">Nonlinearity and optimal component analysis</title><author><persName><forename type="first">W</forename><surname>Mio</surname></persName></author><author><persName><forename type="first">Q</forename><surname>Zhang</surname></persName></author><author><persName><forename type="first">X</forename><surname>Liu</surname></persName></author></analytic><monogr><title level="m">the Proceedings of the International Conference on Neural Networks</title><imprint><date type="published" when="2005"></date></imprint></monogr></biblStruct><biblStruct xml:id="b8"><analytic><title level="a" type="main">On optimal nonlinear associative recall</title><author><persName><forename type="first">T</forename><surname>Poggio</surname></persName></author></analytic><monogr><title level="j">Biological Cybernetics</title><imprint><biblScope unit="volume">19</biblScope><biblScope unit="page" from="201" to="209"></biblScope><date type="published" when="1975"></date></imprint></monogr></biblStruct><biblStruct xml:id="b9"><analytic><title level="a" type="main">Nonlinear component analysis as a kernel eigenvalue problem</title><author><persName><forename type="first">B</forename><surname>Schölkopf</surname></persName></author><author><persName><forename type="first">A</forename><surname>Smola</surname></persName></author><author><persName><forename type="first">K</forename><forename type="middle">R</forename><surname>Müller</surname></persName></author></analytic><monogr><title level="j">Neural Computation</title><imprint><biblScope unit="volume">10</biblScope><biblScope unit="page" from="1299" to="1319"></biblScope><date type="published" when="1998"></date></imprint></monogr></biblStruct><biblStruct xml:id="b10"><analytic><title level="a" type="main">Tools for Application-Driven Dimension Reduction</title><author><persName><forename type="first">A</forename><surname>Srivastava</surname></persName></author><author><persName><forename type="first">X</forename><surname>Liu</surname></persName></author></analytic><monogr><title level="j">Neurocomputing</title><imprint><date type="published" when="2005"></date></imprint></monogr></biblStruct><biblStruct xml:id="b11"><analytic><title></title><author><persName><forename type="first">V</forename><surname>Vapnik</surname></persName></author></analytic><monogr><title level="m">The Nature of Statistical Learning Theory</title><meeting><address><addrLine>New York</addrLine></address></meeting><imprint><publisher>Springer-Verlag</publisher><date type="published" when="1995"></date></imprint></monogr></biblStruct><biblStruct xml:id="b12"><monogr><title level="m" type="main">Foundations of Differentiable Manifolds and Lie Groups</title><author><persName><forename type="first">F</forename><forename type="middle">W</forename><surname>Warner</surname></persName></author><imprint><date type="published" when="1983"></date><publisher>Springer</publisher><pubPlace>New York</pubPlace></imprint></monogr></biblStruct><biblStruct xml:id="b13"><analytic><title level="a" type="main">Kernel optimal component analysis</title><author><persName><forename type="first">Q</forename><surname>Zhang</surname></persName></author><author><persName><forename type="first">X</forename><surname>Liu</surname></persName></author></analytic><monogr><title level="m">the Proceedings of the IEEE Workshop on Learning in Computer Vision and Pattern Recognition</title><imprint><date type="published" when="2004"></date></imprint></monogr></biblStruct></listBibl></back></text></istex:refBibTEI></enrichments></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001789 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 001789 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Ticri/CIDE
|area= OcrV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:0DBA3A1211F38D7A24362213C22F4BFB7EA014E7
|texte= Kernel Methods for Nonlinear Discriminative Data Analysis
}}
| This area was generated with Dilib version V0.6.32. |  |